UK

United Kingdom · Technology and data · Mid-Level (2-5 years)

Quantitative Developer

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

See how AI is changing this job, and get the full read by email.

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  • Experience bandMid-Level (2-5 years)
  • Direct reportsNo direct reports
  • Reports toSenior Quantitative Developer or Lead Quantitative Developer
  • UK framework levelUsually a coordinator, or early in a professional job

Also advertised as Quant Developer · Mid-Level Quant Analyst · Algorithmic Trading Developer

Built on 43,079 real UK job descriptions we analysed · grounded in what real qualifications cover

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

No confident skill match yet for Quantitative Developer. The closest we hold is below.

Qualifi Level 3 Extended Diploma in IT and Data Science Qualifi Ltd · Level 3 · covers 1 of 1 standard

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Machine Learning Methods and Models in Data Science Qualifi Level 3 Extended Diploma in IT and Data Science
Machine Learning Algorithms OCNLR Level 5 Diploma in Software Engineering with Artificial Intelligence
Data Analytics and Machine Learning ATHE Level 5 Extended Diploma in Computing
Machine Learning Pearson BTEC Level 5 Higher National Diploma in Computing for England

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
Systems Fluency seeing the whole picture
Your Plan Illustration

Built for Quantitative Developer

4 units that map to this job, from the qualifications that cover it.

  1. Machine Learning Methods and Models in Data Science Qualifi Ltd · covers 1 of 1 standards Level 3
  2. Machine Learning Algorithms OCN London · covers 1 of 1 standards Level 5
  3. Data Analytics and Machine Learning ATHE Ltd · covers 1 of 1 standards Level 5
  4. Machine Learning Pearson Education Ltd · covers 1 of 1 standards Level 5
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Quantitative Developer?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

  • Prompt Engineering & LLM Integration
  • Explainable AI (XAI) for Financial Models
Why each one is rising

The future skills this job leans on

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

What happens when you start

Next, a free account. We build your Quantitative Developer plan as you set up, starting with the free AI Fluency module. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

Ready to pay? Subscribe for £70 a month

Keep this read. One email with what AI is changing in this job, the new skills it asks for and where to start free. Nothing else follows unless you confirm your address.

3What this role really is

A quantitative developer turns financial models into code that works in the market. The job means understanding the maths, the data, and what each line of code does once it runs. This work builds the engines behind trading strategies, so they run fast, accurate and reliable.

4What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

Python (pandas, NumPy, SciPy, scikit-learn, Statsmodels) Expert

You'll be writing clean, efficient Python scripts daily for data manipulation, statistical analysis, model implementation, and prototyping. You'll use existing libraries and might even contribute to new ones.

C++ Advanced

You'll be reading, debugging, and making significant modifications to existing performance-critical C++ code, such as pricing engines or execution logic. You should understand memory management and be able to implement new features.

Time-Series Databases (Kdb+/q or InfluxQL) Expert

You'll write complex queries in `Kdb+/q` or `InfluxQL` to retrieve, aggregate, and analyse tick-level market data. You'll also be involved in maintaining and optimising existing data schemas.

SQL Databases (PostgreSQL or MS SQL Server) Advanced

Proficiently writing complex joins, subqueries, and Common Table Expressions (CTEs) to query static and reference data. You might also be involved in optimising query performance.

Version Control & CI/CD (Git, Jenkins/GitLab CI) Expert

You'll use Git for all your code management—branching, merging, rebasing, and resolving conflicts. You'll also be comfortable navigating and contributing to CI/CD pipelines to build, test, and deploy your models.

Containerisation & Cloud (Docker, AWS EC2/S3/Lambda/Batch or GCP equivalents) Advanced

You'll write Dockerfiles to containerise your applications for consistent development and deployment. You'll use cloud services (like AWS S3 for storage, Lambda for serverless functions, or Batch for heavy compute) to scale your research and backtesting infrastructure.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing in Where you are nowThe step above
Technical Approach for a New Model Component Proposes an approach to supervisor, who then reviews and approves or modifies it. Rarely makes independent decisions. Researches and proposes several technical approaches, outlining pros and cons. Makes an independent decision within established guidelines, escalating if it deviates significantly or has broader system impact. Designs and defines the technical approach, making independent decisions on architecture and implementation details. Consults with Lead on strategic implications only.
Debugging a Production Issue Identifies the issue, gathers information, and escalates to a senior team member for diagnosis and resolution. Diagnoses the root cause of the issue, proposes a fix, and implements it after a quick review with a senior colleague. Knows when to escalate if the issue is complex or high-impact. Independently diagnoses, designs, and implements complex fixes for critical production issues, coordinating with other teams as needed. May lead the incident response.
Code Review & Standards Receives feedback on their code. Learns and applies team coding standards. Provides constructive feedback in code reviews for peers and junior developers. Ensures their own code adheres to established standards and best practices. Leads code reviews, sets and refines coding standards, and mentors junior developers on best practices. Acts as a gatekeeper for code quality.
Project Timeline & Scope Changes Informs supervisor of any potential delays or scope creep. Does not make changes independently. Identifies potential delays or scope changes, analyses the impact, and proposes adjustments to their manager. Can negotiate minor adjustments within their own tasks. Negotiates and approves minor timeline or scope adjustments within their workstream. Escalates major changes with a clear recommendation to leadership.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

Code Bug Rate
Number of critical bugs found in your code within the first month of production deployment.
Target · <5 bugs per 1,000 lines of code

You deploy a new pricing function. In the first month, only 2 minor bugs are reported in 1,500 lines of code, well within target.

Task Completion Rate
Percentage of assigned development tasks (Jira tickets) completed within their estimated sprint timelines.
Target · 90% or higher

Out of 10 tickets assigned in a sprint, you complete 9 on time, with the 10th being a complex issue that required re-scoping and was moved to the next sprint.

Backtest Accuracy
How closely your implemented model's backtest results match the original research or specified benchmarks.
Target · <0.1% deviation from benchmark PnL or key metrics

The research team's backtest showed a Sharpe ratio of 1.8. Your coded version, when backtested, shows 1.79, which is a deviation of less than 0.1%.

Model Deployment Success Rate
The proportion of your developed model components that are successfully deployed to production without requiring immediate rollbacks or significant hotfixes.
Target · 95% success rate

Over a quarter, you've pushed 20 model updates or new components to production. Only one required a rollback due to an unforeseen edge case, giving you a 95% success rate.

Component Latency Reduction
The measured improvement in execution speed or processing time for specific code components you've optimised.
Target · Achieve a 15% reduction in latency for identified bottlenecks

You take ownership of a slow market data parsing module. After your optimisations, its average processing time drops from 100 microseconds to 80 microseconds, a 20% improvement.

Code Quality & Maintainability
How clean, well-structured, and easy-to-understand your code is, including adherence to coding standards and documentation.
  • Positive feedback in code reviews (e.g., 'clear logic', 'good comments', 'easy to follow'). Minimal technical debt introduced. Colleagues can easily pick up and extend your work.
Problem Solving & Autonomy
Your ability to independently diagnose and solve technical challenges, knowing when to ask for help versus pushing through on your own.
  • You're often unblocking yourself from tricky issues. You come to your manager with proposed solutions, not just problems. You can articulate the trade-offs of different approaches.
Collaboration & Communication
How effectively you work with other developers, researchers, and traders, clearly explaining technical concepts and actively participating in team discussions.
  • You actively participate in design discussions and stand-ups. Colleagues seek your input on their problems. You can explain a complex model's behaviour to a trader without resorting to jargon. You give constructive feedback in code reviews.
Attention to Detail
Your meticulousness in handling data, model parameters, and code, catching errors before they become bigger problems.
  • You spot the off-by-one error in a loop or the subtle data mismatch that others missed. Your test cases cover obscure edge cases. Your documentation is thorough and accurate.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy
Solving Complex Puzzles

You get a real kick out of deconstructing a tricky mathematical problem or figuring out how to make a slow piece of code run lightning fast. The harder the problem, the more engaged you are.

Spending a full day optimising a Monte Carlo simulation, reducing its runtime by 30%, and feeling genuinely satisfied by the achievement.

Seeing Your Code in Action

There's a tangible thrill when you see a model you've built go live and start generating PnL for the firm. You're motivated by the direct impact your work has on the business.

Watching a new execution algorithm you developed successfully place thousands of trades in a day, knowing your code is making real money.

Continuous Learning & Growth

You're always reading up on the latest research, experimenting with new libraries, or trying to understand a new market concept. The idea of standing still technically or intellectually is a turn-off.

Spending your lunch break diving into a paper on reinforcement learning for optimal execution, then trying to apply a concept from it to a current project.

What frustrates people
  • Garbage In, Garbage Out: You'll spend a huge chunk of your time—probably 70%—just cleaning, aligning, and fixing messy market data. Think corporate actions, symbol changes, exchange errors. It's not the exciting part, but it's essential, and it means less time on actual research.
  • The Backtest vs. Reality Gap: You'll build a strategy that looks absolutely brilliant in a backtest, with a Sharpe ratio of 3.0. Then, in live trading, it barely manages 0.5 because of transaction costs, slippage, or model decay that you just couldn't perfectly simulate. It's a humbling experience.
  • Explaining the Unexplainable: Sometimes, you'll have to try and justify to a Portfolio Manager why a complex model, perhaps a gradient boosting machine with 200 features, made a specific trade. It's like trying to explain a dream—the logic isn't always linear or intuitive.
  • The 'Urgent' Request from the Desk: Just when you're deep into a long-term research project, a trader will come over with an 'urgent' request for a 'quick' analysis. This almost always turns into a week-long, fire-fighting exercise that completely derails your plans.
  • Legacy Code Archaeology: You might be asked to fix a bug in a critical 20-year-old Fortran or C++ pricing library. It'll have zero documentation, and the person who wrote it left the firm a decade ago. It's like trying to decipher ancient hieroglyphs.
  • The Politics of PnL Attribution: Getting proper credit for your model's contribution to the desk's profits can be a battle. Traders might claim the success was due to their 'discretionary overlay', even when your model did most of the heavy lifting.
  • Model Risk Anxiety: There's a constant, low-level hum of fear that a hidden bug or a sudden, unexpected 'regime shift' in the market will cause your model to lose a catastrophic amount of money in minutes. It keeps you on your toes.
What this role does not give you
  • A perfectly predictable 9-to-5 routine with no urgent requests.
  • Immediate gratification for every piece of code you write—some projects take months to bear fruit, and some never get deployed.
  • A role where you can avoid the messy, unglamorous work of data cleaning and validation.
  • A purely academic research environment; here, theory has to meet the harsh realities of the market.

8Who you work with

Your work directly supports our trading desks and risk functions. Accurate and efficient code means better trading decisions, reduced operational risk, and ultimately, a healthier bottom line for the firm. You're not just writing code; you're building the intelligence that drives our financial success.

Inside the business
  • Senior Quants (for model specifications and design reviews)
  • Traders and Portfolio Managers (your end-users, who'll tell you what they need and how it's performing)
  • Risk Management Team (who'll scrutinise your models for potential pitfalls)
  • Core Engineering Team (for infrastructure support and deployment pipelines)
  • Product Managers (for defining requirements for new tools or features)
Outside the business
  • Market Data Providers (you'll often deal with their APIs and data feeds)
  • Exchange Connectivity Teams (especially when working on execution algorithms)

9What you need before you start

Not a wish list. The things you would be expected to already have.

  • Proven ability to write production-quality code in Python and C++ (not just academic scripts).
  • A solid foundation in probability, statistics, linear algebra, and calculus.
  • Experience with financial time-series data and its unique challenges (e.g., non-stationarity, fat tails).
  • Demonstrable experience with Git for version control and collaborative development.
  • Experience with SQL and at least one time-series database (e.g., Kdb+).
  • A genuine interest in financial markets and quantitative trading strategies.

10What to practise next

Where the job is going, and what to do about it starting this week.

Advanced C++ Optimisation & Concurrency

As trading strategies become more latency-sensitive, the need for ultra-fast, highly concurrent C++ code is only increasing. You'll need to squeeze every last nanosecond out of your systems.

Lock-free Data Structures · Hardware-level Optimisation · Advanced Concurrency Primitives · Performance Profiling Tools

  • This week: Pick a performance-critical C++ component you're working on and run a basic profiler (e.g., `gprof`) on it.
  • This month: Read a book or take an online course on advanced C++ concurrency (e.g., 'C++ Concurrency in Action').
  • Month 2: Experiment with implementing a lock-free queue or a similar concurrent data structure in a test environment.
  • Month 3: Apply one advanced optimisation technique to a real-world C++ component and measure the performance improvement.

Quick win: Start by profiling your existing C++ code regularly. You'll be surprised where the bottlenecks actually are.

Cloud-Native Architecture for Quant Systems

The ability to scale compute for backtesting, simulations, and data processing on demand in the cloud is becoming non-negotiable. Building 'cloud-native' means more than just running VMs in AWS.

Serverless Computing (AWS Lambda, GCP Cloud Functions) · Managed Data Services (AWS RDS, GCP BigQuery) · Container Orchestration (Kubernetes, AWS ECS/EKS) · Infrastructure as Code (Terraform, CloudFormation) · Cloud Cost Optimisation

  • This week: Complete a basic AWS or GCP 'getting started' tutorial for one service (e.g., S3 or Cloud Storage).
  • This month: Try to deploy a simple Python script as an AWS Lambda function or GCP Cloud Function.
  • Month 2: Experiment with using Docker Compose to define a multi-service application (e.g., a database and your quant service).
  • Month 3: Learn the basics of Terraform and try to provision a simple cloud resource (e.g., an EC2 instance) using code.

Quick win: Get familiar with the AWS or GCP console. Just poke around and see what's available. It's a huge ecosystem.

11Staying current once you are in

What people here do to keep up
  • Actively participate in quantitative finance meetups or online communities (e.g., QuantNet, Wilmott forums).
  • Regularly read academic papers from arXiv or SSRN relevant to quantitative finance and machine learning.
  • Contribute to open-source projects, especially those related to numerical computing or financial modelling.
  • Attend relevant industry conferences or workshops (e.g., QuantMinds, Strata Data) to stay abreast of new developments.
  • Take online courses on advanced topics like high-performance computing, advanced C++, or new machine learning techniques.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Prompt Engineering & LLM Integration

This is already happening, not just 'future'. Competitors are using large language models (LLMs) to draft reports, summarise research, and even assist with code generation in a fraction of the time it used to take. Quants who figure this out will outproduce their peers significantly.

  • Context Windows & Token Limits
  • Temperature Settings
  • RAG Architectures (Retrieval Augmented Generation)
  • Output Validation & Hallucination Detection
  • Prompt Chaining for Complex Analysis

Explainable AI (XAI) for Financial Models

As we use more complex machine learning models (like deep learning or gradient boosting) for trading and risk, regulators and internal risk teams are demanding to know why a model made a particular decision. 'Black box' models are becoming harder to deploy.

  • SHAP Values (SHapley Additive exPlanations)
  • LIME (Local Interpretable Model-agnostic Explanations)
  • Feature Importance Techniques
  • Counterfactual Explanations
  • Model Debugging with XAI

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Stochastic Calculus & Derivatives Pricing
  • Time-Series Analysis
  • Numerical Methods & Optimization
  • Algorithmic Strategy Development
  • Machine Learning for Finance
  • Risk Modelling & Management

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    Junior Quantitative Developer (L1)

    1-2 years

    Skills to master

    • Solid Python/C++ fundamentals, understanding of basic financial concepts, rigorous testing practices, and effective version control with Git.

    You're ready to move on when

    • Consistently delivers bug-free code for assigned tasks.
    • Can independently debug and resolve minor production issues.
    • Actively contributes to team discussions and asks insightful questions.
    • Demonstrates a clear understanding of the models they are implementing.
  2. 2

    Software Engineer (with Quant Interest)

    2-3 years

    Skills to master

    • Transitioning from general software engineering to financial domain knowledge, numerical methods, and specific quant libraries. Picking up C++ performance optimisation is key.

    You're ready to move on when

    • Has built and deployed high-performance systems in other domains.
    • Demonstrates a strong aptitude for mathematics and statistics.
    • Has actively self-studied quantitative finance concepts.
    • Can translate complex requirements into robust, scalable code.
  3. 3

    Data Scientist (with Strong Programming Skills)

    2-4 years

    Skills to master

    • Deepening knowledge of financial market microstructure, time-series specific modelling, and the low-latency requirements of trading systems. Less about general ML, more about specific quant applications.

    You're ready to move on when

    • Excels at feature engineering and model building with messy data.
    • Can effectively communicate complex statistical findings.
    • Has experience with large-scale data processing and analysis.
    • Shows a keen interest in the financial applications of their data science skills.

15Where this role leads

The long view: This work can lead to lead or staff quantitative developer roles, or on to principal quantitative developer.

16Pay and demand

Pay & demand

Typical pay £53,295 a year Source: ONS Annual Survey of Hours and Earnings, 2025 (provisional), under the Open Government Licence v3.0

The figure is the median for full-time employees in the ONS occupation this job title codes to (Management consultants and business analysts), from the April 2025 survey, about six months old when published, as ASHE always is. It is that occupation's middle, not this role's. Half earn more.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Quantitative Developer is actually changing.

Where it leads

Roles this leads to

The record names a next step, but the catalogue has no page for it yet:

  • Senior Quantitative Developer (L3)

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Machine Learning Methods and Models in Data Science, worked against Quantitative Developer.

Machine Learning Methods and Models in Data Science Level 3

Applied to your work in Quantitative Developer

The objective of this unit is to provide learners with a foundational understanding of machine learning methods and models used in data science. Learners will gain knowledge of supervised, unsupervised, and reinforcement learning, including their applications and key characteristics.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

See Your Progress Grow Illustration
Quantitative Developer
  • Stochastic Calculus & Derivatives Pricing
  • Time-Series Analysis
  • Numerical Methods & Optimization
  • Algorithmic Strategy Development
  • Machine Learning for Finance
  • Risk Modelling & Management
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

17The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Quantitative Developer is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Senior Quantitative Developer (L3)

    3-5 years in current role

    You'll move from owning specific components to designing and leading entire workstreams. You'll also start mentoring junior colleagues.

    • Model Design & Architecture Designing new, complex models and systems from scratch, not just implementing components.
    • Advanced Optimisation Deeper expertise in C++ and Python performance tuning for ultra-low latency systems.
    • Mentorship & Code Quality Guardianship Taking responsibility for the technical growth of junior team members and upholding code quality standards across the team.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, a lot of what we do as quant developers is repetitive or involves sifting through mountains of information. Imagine if you could offload some of that grunt work and focus on the really interesting, high-impact stuff. That's exactly what AI tools are starting to do for us.

We're not talking about AI replacing you; we're talking about AI making you a super-quant. These tools can handle the tedious bits, from writing boilerplate code to summarising dense research papers, giving you back precious time to focus on model design, complex problem-solving, and finding that elusive alpha. Think of AI as your incredibly efficient (and tireless) junior assistant.

Automated Test Generation for Models

Imagine feeding your complex C++ or Python pricing function into an AI and having it automatically spit out a comprehensive suite of unit tests. This includes tricky edge cases and boundary conditions that you might have missed. It means more robust code, faster development cycles, and fewer production bugs. No more writing tedious tests by hand.

Academic Paper & Research Synthesis

You know how much time goes into sifting through arXiv or SSRN for the latest quant papers? Now, you can feed those dense academic articles into an LLM and get concise summaries of new methodologies, mathematical proofs, and potential applications for alpha generation. It's like having a research assistant who's read everything and can tell you what actually matters.

Alternative Data Feature Discovery

We're drowning in alternative data—satellite imagery, credit card transactions, news sentiment. Using machine learning models, like autoencoders or LLMs on text data, you can scan these vast datasets and have the AI suggest novel features for your trading models. This dramatically accelerates your research discovery process, potentially uncovering new sources of alpha in weeks, not months.

Smart Model Documentation & Validation Reporting

Model Risk Management needs comprehensive documentation, and frankly, writing it is a chore. Use AI to generate the first draft of your model documentation, including descriptions of data sources, methodology, assumptions, and limitations. It's not perfect, but it gives you a massive head start, especially during those busy month-end or quarterly cycles.

Common questions

Common questions

How do you become a Quantitative Developer?

Common routes in include Junior Quantitative Developer (1-2 years), Software Engineer (with Quant Interest) (2-3 years) and Data Scientist (with Strong Programming Skills) (2-4 years). Times vary with prior experience.

Where can a Quantitative Developer progress to?

This role can lead on to Senior Quantitative Developer (3-5 years in current role), depending on the skills you build.

What new skills matter most for a Quantitative Developer?

Increasingly, Prompt Engineering & LLM Integration and Explainable AI (XAI) for Financial Models. These are the skills our record of this role lists as growing.

Free tools

Want a read on where you stand?

Three free tools, no account needed. Each one ends with a role, a level and a way in.

Read the Zavmo manifesto to understand how you will be taught Meet your tutors

Or try another one

18Where to go from here

Other roles at Level 3

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

 More jobs in technology and data

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills built as a quantitative developer carry into broader data science, machine learning engineering, or software architecture in other data-heavy industries. Strong analytical and programming skills apply well beyond one sector.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my free AI Fluency module What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.